Skip to main content
Have a personal or library account? Click to login
Mastering PyTorch Cover

Mastering PyTorch

Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

Publisher:Packt Publishing Limited
By:   
Paid access
|Sep 2024
Product purchase options
This title is not yet available for download
Description

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples

Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks
Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

  • Understand how to use PyTorch to build advanced neural network models
  • Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker
  • Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Book Description

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.
By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What you will learn

  • Implement text, vision, and music generation models using PyTorch
  • Build a deep Q-network (DQN) model in PyTorch
  • Deploy PyTorch models on mobile devices (Android and iOS)
  • Become well versed in rapid prototyping using PyTorch with fastai
  • Perform neural architecture search effectively using AutoML
  • Easily interpret machine learning models using Captum
  • Design ResNets, LSTMs, and graph neural networks (GNNs)
  • Create language and vision transformer models using Hugging Face

Who this book is for

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Table of Contents

  1. Overview of Deep Learning using PyTorch
  2. Deep CNN architectures
  3. Combining CNNs and LSTMs
  4. Deep Recurrent Model Architectures
  5. Advanced Hybrid Models
  6. Graph Neural Networks
  7. Music and Text Generation with PyTorch
  8. Neural Style Transfer
  9. Deep Convolutional GANs
  10. Image Generation Using Diffusion
  11. Deep Reinforcement Learning
  12. Model Training Optimizations
  13. Operationalizing PyTorch Models into Production
  14. PyTorch on Mobile Devices
  15. Rapid Prototyping with PyTorch
  16. PyTorch and AutoML
  17. PyTorch and Explainable AI
  18. Recommendation Systems with TorchRec
  19. PyTorch and Hugging Face
https://github.com/arj7192/MasteringPyTorchV2
PDF ISBN: 978-1-80107-996-9
Publisher: Packt Publishing Limited
Copyright owner: © 2024 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 554
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM044000,
COM025000,
COM072000
Thema subjects:
UYQN,
UYQE,
UYM

People also read